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Aiming at the problems of poor optimization ability, easy deadlock, and low search efficiency of the traditional ant colony optimization (ACO) when applied to mobile robot path planning, a multilevel field of view adaptive ant colony optimization (MLFVAACO) algorithm was proposed. Firstly, on the basis of ACO, the two levels field of view was expanded sequentially to make the planned path smooth. Secondly, an adaptive global initial pheromone update strategy was designed, which not only avoided the blind search phenomenon of ants in the early stage of the algorithm but also strengthened the guiding role of ants in selecting optional areas. Then the deadlock ants in the algorithm iteration process were optimized to improve the utilization of the ant colony and increase the diversity of search solutions. Finally, the state transition rule of ants was improved to prevent ants from falling into the local optimal solution. The optimal parameters of the MLFVAACO algorithm were selected through simulation analysis, and the feasibility and effectiveness of the MLFVAACO algorithm were verified by comparing it with the traditional ACO algorithm, the improved ACO algorithms, and the graph search algorithms, respectively, in two kinds of grid maps with different levels of complexity. The simulation results showed that in simple and complex environments, compared with the traditional ACO algorithm, the optimal path of the MLFVAACO algorithm was shortened by 12.74% and 4.38%, respectively, the turning points of the path were reduced by 50% and 63.16%, respectively, the ant utilization rate was increased by 99.99% and 99.95%, respectively, and the search efficiency was increased by 60.14% and 62.17%, respectively. Compared with the improved ACO algorithms and the graph search algorithms, MLFVAACO algorithm can plan the shortest path with better path smoothness, while the quality of the search solutions was also better. This fully validated the excellent performance of MLFVAACO algorithm when applied to mobile robot path planning.